Repository containing the Python version of DIGNEA, a Diverse Instance Generator with Novelty Search and Evolutionary Algorithms. This framework is an extensible tool for generating diverse and discriminatory instances for any desired domain. The instances obtained generated will be biased to the performance of a target in a specified portfolio of algorithms.
- Numpy
- Sklearn
- Pandas
- Keras
- DEAP
- Tensorflow (CPU version)
DIGNEA was used in the following publications:
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Alejandro Marrero, Eduardo Segredo, and Coromoto Leon. 2021. A parallel genetic algorithm to speed up the resolution of the algorithm selection problem. Proceedings of the Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, New York, NY, USA, 1978–1981. DOI:https://doi.org/10.1145/3449726.3463160
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Marrero, A., Segredo, E., León, C., Hart, E. (2022). A Novelty-Search Approach to Filling an Instance-Space with Diverse and Discriminatory Instances for the Knapsack Problem. In: Rudolph, G., Kononova, A.V., Aguirre, H., Kerschke, P., Ochoa, G., Tušar, T. (eds) Parallel Problem Solving from Nature – PPSN XVII. PPSN 2022. Lecture Notes in Computer Science, vol 13398. Springer, Cham. https://doi.org/10.1007/978-3-031-14714-2_16